Introduction: Chest computed tomography (CT) interpretation is a key competency for pulmonary fellows, with many resources intended for radiologists but very few for this specific group. We endeavored to create a curriculum to teach chest CT interpretation to first-year pulmonary fellows.
Methods: We assembled a team of two pulmonologists, one radiologist, and a fellow with computer drafting software experience. We reviewed the literature, used principles of cognitive load theory to outline the content of our curriculum, collected original CT images exemplifying key patterns of disease, created original illustrations using computer drafting programs, and outlined frameworks to identify chest CT patterns and build differential diagnoses. We divided the material into five short videos and provided 18 practice cases to be reviewed asynchronously. We then organized a 1-hour in-person review session facilitated by a chest radiologist. We created a survey to assess our curriculum. The material presented here has been delivered to three consecutive classes of first-year pulmonary and critical care medicine fellows at our institution.
Results: Nineteen fellows in three cohorts reviewed the curriculum. Twelve fellows (63% response rate) completed the postcurriculum survey. Overall, there was a significant improvement in comfort, with the calculated paired sample test showing a mean comfort of 3.2 precurriculum and a mean comfort of 4.5 postcurriculum ( < .001).
Discussion: This self-guided, interactive curriculum provides a structured approach connecting key lung anatomy to patterns of disease and is an effective way to teach chest CT interpretation to pulmonary fellows.
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http://dx.doi.org/10.15766/mep_2374-8265.11481 | DOI Listing |
MedEdPORTAL
January 2025
Associate Professor, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington School of Medicine; Staff Physician, Pulmonary, Critical Care and Sleep Medicine Section, Veterans Affairs Puget Sound Healthcare System.
Introduction: Chest computed tomography (CT) interpretation is a key competency for pulmonary fellows, with many resources intended for radiologists but very few for this specific group. We endeavored to create a curriculum to teach chest CT interpretation to first-year pulmonary fellows.
Methods: We assembled a team of two pulmonologists, one radiologist, and a fellow with computer drafting software experience.
Heliyon
January 2025
Chest Clinical College of Tianjin Medical University, Tianjin, 300270, China.
Backgroud: Fluid volume abnormalities are a major cause of exacerbations in heart failure patients. However, there is few efficient, rapid, or cost-effective clinical approach for determining volume status, resulting in inadequate or unsatisfactory treatment. The aim was to develop an early fluid volume detection model for heart failure patients utilizing a machine learning stratification.
View Article and Find Full Text PDFMedicine (Baltimore)
November 2024
Department of Radiology, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich, Baden, Switzerland.
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection.
View Article and Find Full Text PDFIntroduction: A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormalities on CXRs, which may expand diagnostic support to a wider field of health professionals. There is a paucity of evidence on the impact of AI algorithms in assisting healthcare professionals (other than radiologists) who regularly review CXR images in their daily practice.
View Article and Find Full Text PDFChest
January 2025
Respiratory Research@Alfred, Central Clinical School, Monash University, VIC, Australia; Institute for Breathing and Sleep, VIC, Australia; Department of Physiotherapy, Alfred Health, VIC, Australia.
Background: Pulmonary rehabilitation (PR) is a beneficial intervention for people with interstitial lung disease (ILD), however the effect of PR on survival is unclear. This study compared the survival outcomes in people with ILD who were allocated to PR versus those who were allocated to control in two published randomised controlled trials (RCTs).
Research Question: Does participation in PR impact survival among people with ILD?
Study Design And Methods: The combined data from the two previous RCTs of PR in ILD were included.
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